Do you ever feel like you’re running out of interesting project ideas to work on? Okay, not after you’ve read this blog. Artificial intelligence has become a common topic in movies, novels, and other types of media. How about you create an AI project by yourself ? Sounds cool?
Machines or computers that imitate human mind functions such as learning and problem-solving are referred to as artificial intelligence.Because we care about you, we’ve compiled a list of some fantastic AI project ideas.
Why Should You Do an AI-Based Project?
There are several advantages of working on an artificial intelligence project. AI is a broad and diverse area of study. Furthermore, it involves a significant level of technical understanding, which you’ll eventually acquire and can improve upon them.
These projects will help you improve your skills while also putting your existing knowledge to test. AI may be used in a variety of fields. As you try-out different Artificial Intelligence project concepts, you will get more competence.
With these projects, you obtain hands-on experience. You get to try out new things and have a better understanding of how everything works. It’s the most effective approach to learn about the real-world applications of AI.
Projects involving artificial intelligence span a wide range of sectors and topics. You would not understand what challenges they present unless you tackle them yourself. You will become more proficient with AI as a result of performing these projects.
While working on a python project, you will need to familiarize yourself with new tools and technologies. The more you learn about cutting-edge development tools, environments, and libraries, the more you’ll be able to try out new things with your projects. You will get more experience as you explore various AI project concepts.
Artificial Intelligence Project Ideas
1. Handwritten Digits Recognition
Developing a Handwritten Digits Recognition System that can recognise the numbers drawn by humans is a fantastic way to get started with artificial intelligence. The ability of computers to identify human handwritten digits is known as handwritten digit recognition. Human-written digits have a wide range of curves and sizes since they are hand-drawn, and no two people write in the same way. The answer to this problem is this project, which uses the picture of a digit to recognise the digit written in it. This project requires a basic understanding of Python programming, deep learning with the Keras library, and the Tkinter toolkit for building graphical user interfaces(GUI). Download the full source code for the project and access the code from here.
2. Detecting Spam Comments on YouTube
YouTube’s success attracted not just real viewers, but also spammers. As a result, the number of undesirable spam videos and comments has increased. Here’s where an AI-based YouTube spam comment detection algorithm comes handy. You will use text and words to classify online comments as spam or not spam in this AI project.Using bag-of-words and random forest approaches, a spam detection model may be created. Try out this project by accessing it from here: https://archive.ics.uci.edu/ml/datasets/YouTube+Spam+Collection
3. Predicting Species of Birds
Birds are ecological indicators, and they react quickly to changes in the environment. As a result, it is critical to classify birds appropriately in order to comprehend ecological issues. Manual categorization of birds may be done by domain specialists, but due to the massive increase in data, it has become a tiresome and time-consuming procedure.This is where artificial intelligence-based categorization becomes crucial. There are two approaches to this classification-based AI project. A random forest can be used to forecast bird species if you are a beginner. A Convolution neural network can be used to achieve an intermediate level. The dataset for this project can be accessed from here- Caltech-UCSD Birds-200–2011
4.XTREME
XTREME is a pre-trained multilingual model that has tasks which require reasoning. It includes 40 typologically distinct languages and has 9 tasks to perform. XTREME is a Google artificial intelligence project that uses natural language processing to perform sentence categorization, structured prediction, sentence recovery, and question answering. Try out from this source: https://opensource.google/projects/xtreme
5. MEENA
MEENA is a chatbot that can handle a wide range of discussion topics and improves foreign language learning, among other things. With a single Evolved Transformer encoder block and 13 Evolved Transformer decoder blocks, it’s an end-to-end trained Neural Conversational Model. These blocks assist the bot in responding intelligently by reducing confusion and uncertainty in the prediction. Chat with MEENA from here: https://ai.googleblog.com/2020/01/towards-conversational-agent-that-can.html
6. Lane Line Detection
Lane line detection is an important part of self-driving automobiles and computer vision in general. This concept is used to define the path taken by self-driving automobiles in order to avoid getting into the wrong lane.We would create a machine learning project to recognise lane lines for the automobile in real-time with this project. This would be achieved with the help of the OpenCV library and computer vision ideas.Access the source code from here: Lane Line Detection Project Source Code
7. Enron
Enron was one of the major energy companies in the United States until it collapsed overnight. With the use of emails written by Enron’s former senior leaders, this AI research analyses the company’s fraud practises. There are 500 thousand emails from former workers on the server. The Enron database can be found at the URL : Enron Email Dataset . A guide to access the data- Enron Dataset Report.
8. Food Attribute Classification
This AI-powered initiative categorises a wide range of foods based on the cuisine and taste with the help of a multi-scale convolutional network, to build a deep learning model. Yummly48k is a food attribute dataset extracted from the Yummly website. It also employs Negative Log-Likelihood (NLL) for development of the model.
9. G-mail Smart Reply
Gmail’s smart reply feature suggests email responses based on a machine-learning algorithm. It is built on a revolutionary thinking hierarchy in which each hierarchical model is capable of learning, remembering, and recognising a sequential pattern. When replying, it assesses if the gesture is positive or negative.It makes use of technologies such as long-short-term-memory (LSTM) recurrent neural networks and semantics. Try this with : https://ai.googleblog.com/2017/05/efficient-smart-reply-now-for-gmail.
10. Forecasting Earthquake Aftershock Locations
AI-assisted technology is being used to forecast the locations of earthquake aftershocks. Earthquakes cause widespread devastation everywhere. It begins with a mainshock and is followed by a series of aftershocks. Empirical laws may be used to predict the size and timing of aftershocks, but projecting their locations remains difficult. The AI project developed by Google applies deep learning to determine the location where an aftershock may occur. The project makes use of data from 118 major earthquakes that have been reported throughout the world. It analyses the static stress change of mainshock and aftershock regions using a neural network.For trying it out and any assistance with this project, visit:Forecasting earthquake aftershock locations with AI-assisted science.
The greatest way to learn about anything is to do it yourself, and these DIY projects are ideal for studying various aspects of AI technology since they not only assist in learning and implementing principles, but they are also interesting. Most of these projects require a PC or laptop as well as an internet connection and are suitable for children aged 9 to 15. You can easily try them at home, and each project would take approximately 30–45 minutes only. Get started with these fun projects today for an enriching experience.